OpenRaiser/Intern-Atlas huggingface dataset:
Bridging the Language Gap: Knowledge Injected Multilingual Question Answering
Venue: arXiv cs.CL 2023
Year: 2023

Question Answering (QA) is the task of automatically answering questions posed by humans in natural languages. There are different settings to answer a question, such as abstractive, extractive, boolean, and multiple-choice QA. As a popular topic in natural language processing tasks, extractive question answering task (extractive QA) has gained extensive attention in the past few years. With the continuous evolvement of the world, generalized cross-lingual transfer (G-XLT), where question and answer context are in different languages, poses some unique challenges over cross-lingual transfer (XLT), where question and answer context are in the same language. With the boost of corresponding development of related benchmarks, many works have been done to improve the performance of various language QA tasks. However, only a few works are dedicated to the G-XLT task. In this work, we propose a generalized cross-lingual transfer framework to enhance the model's ability to understand different languages. Specifically, we first assemble triples from different languages to form multilingual knowledge. Since the lack of knowledge between different languages greatly limits models' reasoning ability, we further design a knowledge injection strategy via leveraging link prediction techniques to enrich the model storage of multilingual knowledge. In this way, we can profoundly exploit rich semantic knowledge. Experiment results on real-world datasets MLQA demonstrate that the proposed method can improve the performance by a large margin, outperforming the baseline method by 13.18%/12.00% F1/EM on average.
Spectral Toolkit of Algorithms for Graphs: Technical Report (1)
Venue: arXiv cs.SI 2023
Year: 2023

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022. We have so far finished the component on local graph clustering, and this technical report presents a user's guide to STAG, showcase studies, and several technical considerations behind our development.
Unifying Linearity and Dependency Analyses
Venue: arXiv cs.PL 2023
Year: 2023

Linearity and dependency analyses are key to several applications in computer science, especially, in resource management and information flow control. What connects these analyses is that both of them need to model at least two different worlds with constrained mutual interaction. Though linearity and dependency analyses address similar problems, the analyses are carried out by employing different methods. For linearity analysis, type systems employ the comonadic exponential modality from Girard's linear logic. For dependency analysis, type systems employ the monadic modality from Moggi's computational metalanguage. Owing to this methodical difference, a unification of the two analyses, though theoretically and practically desirable, is not straightforward. Fortunately, with recent advances in graded-context type systems, it has been realized that linearity and dependency analyses can be viewed through the same lens. However, existing graded-context type systems fall short of a unification of linearity and dependency analyses. The problem with existing graded-context type systems is that though their linearity analysis is general, their dependency analysis is limited, primarily because the graded modality they employ is comonadic and not monadic. In this paper, we address this limitation by systematically extending existing graded-context type systems so that the graded modality is both comonadic and monadic. This extension enables us to unify linearity analysis with a general dependency analysis. We present a unified Linear Dependency Calculus, LDC, which analyses linearity and dependency using the same mechanism in an arbitrary Pure Type System. We show that LDC is a general linear and dependency calculus by subsuming into it the standard calculi for the individual analyses.
Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes
Venue: arXiv cs.GR 2023
Year: 2023

This report surveys advances in deep learning-based modeling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modeling tasks based on these representations. Specifically, we focus on the analysis and synthesis of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks -- 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model-driven methods that allow for human-centric, progressive scene synthesis. We identify the challenges involved in modeling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state-of-the-art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output, etc., providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse.
Toward End-to-End MLOps Tools Map: A Preliminary Study based on a Multivocal Literature Review
Venue: arXiv cs.SE 2023
Year: 2023

MLOps tools enable continuous development of machine learning, following the DevOps process. Different MLOps tools have been presented on the market, however, such a number of tools often create confusion on the most appropriate tool to be used in each DevOps phase. To overcome this issue, we conducted a multivocal literature review mapping 84 MLOps tools identified from 254 Primary Studies, on the DevOps phases, highlighting their purpose, and possible incompatibilities. The result of this work will be helpful to both practitioners and researchers, as a starting point for future investigations on MLOps tools, pipelines, and processes.
Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms
Venue: arXiv cs.LG 2023
Year: 2023

In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS), which is a general-purpose cognitive reasoning framework. In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks. To investigate this, we conduct a comparative analysis of the performance of ONA as an implementation of NARS and $Q$-Learning in various environments that were created using the Open AI gym. The environments have different difficulty levels, ranging from simple to complex. Our results demonstrate that NARS is a promising alternative to RL, with competitive performance in diverse environments, particularly in non-deterministic ones.
Automatic Detection of Reactions to Music via Earable Sensing
Venue: arXiv cs.SD 2023
Year: 2023

We present GrooveMeter, a novel system that automatically detects vocal and motion reactions to music via earable sensing and supports music engagement-aware applications. To this end, we use smart earbuds as sensing devices, which are already widely used for music listening, and devise reaction detection techniques by leveraging an inertial measurement unit (IMU) and a microphone on earbuds. To explore reactions in daily music-listening situations, we collect the first kind of dataset, MusicReactionSet, containing 926-minute-long IMU and audio data with 30 participants. With the dataset, we discover a set of unique challenges in detecting music listening reactions accurately and robustly using audio and motion sensing. We devise sophisticated processing pipelines to make reaction detection accurate and efficient. We present a comprehensive evaluation to examine the performance of reaction detection and system cost. It shows that GrooveMeter achieves the macro F1 scores of 0.89 for vocal reaction and 0.81 for motion reaction with leave-one-subject-out cross-validation. More importantly, GrooveMeter shows higher accuracy and robustness compared to alternative methods. We also show that our filtering approach reduces 50% or more of the energy overhead. Finally, we demonstrate the potential use cases through a case study.
ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about
Venue: arXiv cs.CL 2023
Year: 2023

Large language models have gained considerable interest for their impressive performance on various tasks. Among these models, ChatGPT developed by OpenAI has become extremely popular among early adopters who even regard it as a disruptive technology in many fields like customer service, education, healthcare, and finance. It is essential to comprehend the opinions of these initial users as it can provide valuable insights into the potential strengths, weaknesses, and success or failure of the technology in different areas. This research examines the responses generated by ChatGPT from different Conversational QA corpora. The study employed BERT similarity scores to compare these responses with correct answers and obtain Natural Language Inference(NLI) labels. Evaluation scores were also computed and compared to determine the overall performance of GPT-3 \& GPT-4. Additionally, the study identified instances where ChatGPT provided incorrect answers to questions, providing insights into areas where the model may be prone to error.
Maximal Ordinal Two-Factorizations
Venue: arXiv cs.AI 2023
Year: 2023

Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.
Constraint Inference in Control Tasks from Expert Demonstrations via Inverse Optimization
Venue: arXiv cs.RO 2023
Year: 2023

Inferring unknown constraints is a challenging and crucial problem in many robotics applications. When only expert demonstrations are available, it becomes essential to infer the unknown domain constraints to deploy additional agents effectively. In this work, we propose an approach to infer affine constraints in control tasks after observing expert demonstrations. We formulate the constraint inference problem as an inverse optimization problem, and we propose an alternating optimization scheme that infers the unknown constraints by minimizing a KKT residual objective. We demonstrate the effectiveness of our method in a number of simulations, and show that our method can infer less conservative constraints than a recent baseline method, while maintaining comparable safety guarantees.
Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization
Venue: arXiv cs.LG 2023
Year: 2023

Automated machine learning (AutoML) methods improve upon existing models by optimizing various aspects of their design. While present methods focus on hyperparameters and neural network topologies, other aspects of neural network design can be optimized as well. To further the state of the art in AutoML, this dissertation introduces techniques for discovering more powerful activation functions and establishing more robust weight initialization for neural networks. These contributions improve performance, but also provide new perspectives on neural network optimization. First, the dissertation demonstrates that discovering solutions specialized to specific architectures and tasks gives better performance than reusing general approaches. Second, it shows that jointly optimizing different components of neural networks is synergistic, and results in better performance than optimizing individual components alone. Third, it demonstrates that learned representations are easier to optimize than hard-coded ones, creating further opportunities for AutoML. The dissertation thus makes concrete progress towards fully automatic machine learning in the future.
Leveraging Reusability: Improved Competitive Ratio of Greedy for Reusable Resources
Venue: arXiv cs.DS 2023
Year: 2023

We study online weighted bipartite matching of reusable resources where an adversarial sequence of requests for resources arrive over time. A resource that is matched is 'used' for a random duration, drawn independently from a resource-dependent distribution, after which it returns and is able to be matched again. We study the performance of the greedy policy, which matches requests to the resource that yields the highest reward. Previously, it was known that the greedy policy is 1/2 competitive against a clairvoyant benchmark that knows the request sequence in advance. In this work, we improve this result by introducing a parameter that quantifies the degree of reusability of the resources. Specifically, if p represents the smallest probability over the usage distributions that a matched resource returns in one time step, the greedy policy achieves a competitive ratio of $1/(2-p)$. Furthermore, when the usage distributions are geometric, we establish a stronger competitive ratio of $(1+p)/2$, which we demonstrate to be tight. Both of these results align with the known results in the two extreme scenarios: p = 0 corresponds to non-reusable resources, where 1/2 is known to be tight, while p = 1 corresponds to every resource returning immediately, where greedy is the optimal policy and hence the competitive ratio is 1. Finally, we show that both results are robust to approximations of the greedy policy. Our work demonstrates that the reusability of resources can enhance performance compared to the non-reusable setting, and that a simple greedy policy suffices when the degree of reusability is high. Our insights contribute to the understanding of how resource reusability can influence the performance of online algorithms, and highlight the potential for improved performance as the degree of reusability increases.
Self-Supervised Video Similarity Learning
Venue: arXiv cs.CV 2023
Year: 2023

We introduce S$^2$VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learning and address multiple retrieval and detection tasks at once with no use of labeled data. This is achieved by learning via instance-discrimination with task-tailored augmentations and the widely used InfoNCE loss together with an additional loss operating jointly on self-similarity and hard-negative similarity. We benchmark our method on tasks where video relevance is defined with varying granularity, ranging from video copies to videos depicting the same incident or event. We learn a single universal model that achieves state-of-the-art performance on all tasks, surpassing previously proposed methods that use labeled data. The code and pretrained models are publicly available at: https://github.com/gkordo/s2vs
Using LSTM and GRU With a New Dataset for Named Entity Recognition in the Arabic Language
Venue: arXiv cs.CL 2023
Year: 2023

Named entity recognition (NER) is a natural language processing task (NLP), which aims to identify named entities and classify them like person, location, organization, etc. In the Arabic language, we can find a considerable size of unstructured data, and it needs to different preprocessing tool than languages like (English, Russian, German...). From this point, we can note the importance of building a new structured dataset to solve the lack of structured data. In this work, we use the BIOES format to tag the word, which allows us to handle the nested name entity that consists of more than one sentence and define the start and the end of the name. The dataset consists of more than thirty-six thousand records. In addition, this work proposes long short term memory (LSTM) units and Gated Recurrent Units (GRU) for building the named entity recognition model in the Arabic language. The models give an approximately good result (80%) because LSTM and GRU models can find the relationships between the words of the sentence. Also, use a new library from Google, which is Trax and platform Colab
Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start
Venue: arXiv cs.CL 2023
Year: 2023

News sources undergo the process of selecting newsworthy information when covering a certain topic. The process inevitably exhibits selection biases, i.e. news sources' typical patterns of choosing what information to include in news coverage, due to their agenda differences. To understand the magnitude and implications of selection biases, one must first discover (1) on what topics do sources typically have diverging definitions of "newsworthy" information, and (2) do the content selection patterns correlate with certain attributes of the news sources, e.g. ideological leaning, etc. The goal of the paper is to investigate and discuss the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora, without relying on labeled data. To facilitate research in this domain, we propose and study a conceptual framework, where we compare how sources typically mention certain controversial entities, and use such as indicators for the sources' content selection preferences. We empirically show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide. We demonstrate an unsupervised representation learning method to capture the selection preferences for how sources typically mention controversial entities. Our experiments show that that distributional divergence of such representations, when studied collectively across entities and news sources, serve as good indicators for an individual source's ideological leaning. We hope our findings will provide insights for future research on media selection biases.
Runtime Variation in Big Data Analytics
Venue: arXiv cs.DC 2023
Year: 2023

The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able to predict and adjust for them is crucial to cloud service providers to design reliable data processing pipelines, provision and allocate resources, adjust pricing services, meet SLOs and debug performance hazards. In this paper, we analyze the runtime variation of millions of production SCOPE jobs on Cosmos, an exabyte-scale internal analytics platform at Microsoft. We propose an innovative 2-step approach to predict job runtime distribution by characterizing typical distribution shapes combined with a classification model with an average accuracy of >96%, out-performing traditional regression models and better capturing long tails. We examine factors such as job plan characteristics and inputs, resource allocation, physical cluster heterogeneity and utilization, and scheduling policies. To the best of our knowledge, this is the first study on predicting categories of runtime distributions for enterprise analytics workloads at scale. Furthermore, we examine how our methods can be used to analyze what-if scenarios, focusing on the impact of resource allocation, scheduling, and physical cluster provisioning decisions on a job's runtime consistency and predictability.
Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic
Venue: arXiv cs.CY 2023
Year: 2023

The outbreak of COVID-19 has led to a global surge of Sinophobia partly because of the spread of misinformation, disinformation, and fake news on China. In this paper, we report on the creation of a novel classifier that detects whether Chinese-language social media posts from Twitter are related to fake news about China. The classifier achieves an F1 score of 0.64 and an accuracy rate of 93%. We provide the final model and a new training dataset with 18,425 tweets for researchers to study fake news in the Chinese language during the COVID-19 pandemic. We also introduce a new dataset generated by our classifier that tracks the dynamics of fake news in the Chinese language during the early pandemic.
Clique Densification in Networks
Venue: arXiv cs.SI 2023
Year: 2023

Real-world networks are rarely static. Recently, there has been increasing interest in both network growth and network densification, in which the number of edges scales superlinearly with the number of nodes. Less studied but equally important, however, are scaling laws of higher-order cliques, which can drive clustering and network redundancy. In this paper, we study how cliques grow with network size, by analyzing several empirical networks from emails to Wikipedia interactions. Our results show superlinear scaling laws whose exponents increase with clique size, in contrast to predictions from a previous model. We then show that these results are in qualitative agreement with a new model that we propose, the Local Preferential Attachment Model, where an incoming node links not only to a target node but also to its higher-degree neighbors. Our results provide new insights into how networks grow and where network redundancy occurs.
Architecture-Preserving Provable Repair of Deep Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Deep neural networks (DNNs) are becoming increasingly important components of software, and are considered the state-of-the-art solution for a number of problems, such as image recognition. However, DNNs are far from infallible, and incorrect behavior of DNNs can have disastrous real-world consequences. This paper addresses the problem of architecture-preserving V-polytope provable repair of DNNs. A V-polytope defines a convex bounded polytope using its vertex representation. V-polytope provable repair guarantees that the repaired DNN satisfies the given specification on the infinite set of points in the given V-polytope. An architecture-preserving repair only modifies the parameters of the DNN, without modifying its architecture. The repair has the flexibility to modify multiple layers of the DNN, and runs in polynomial time. It supports DNNs with activation functions that have some linear pieces, as well as fully-connected, convolutional, pooling and residual layers. To the best our knowledge, this is the first provable repair approach that has all of these features. We implement our approach in a tool called APRNN. Using MNIST, ImageNet, and ACAS Xu DNNs, we show that it has better efficiency, scalability, and generalization compared to PRDNN and REASSURE, prior provable repair methods that are not architecture preserving.